Density Based Algorithm With Automatic Parameters Generation

TL;DR

This paper introduces an enhanced density-based clustering algorithm that automatically generates parameters, detects arbitrary shaped clusters, recognizes noise, and improves memory efficiency using kdtree, addressing limitations of traditional methods.

Contribution

The proposed algorithm advances density-based clustering by enabling automatic parameter generation and noise recognition, handling complex objects more effectively.

Findings

Capable of detecting arbitrary shaped clusters

Handles complex objects with high accuracy

Improves memory efficiency using kdtree

Abstract

The traditional algorithms do not meet the latest multiple requirements simultaneously for objects. Density-based method is one of the methodologies, which can detect arbitrary shaped clusters where clusters are defined as dense regions separated by low density regions. In this paper, we present a new clustering algorithm to enhance the density-based algorithm DBSCAN. This enables an automatic parameter generation strategy to create clusters with different densities and enables noises recognition, and generates arbitrary shaped clusters. The kdtree is used for increasing the memory efficiency. Experimental result shows that proposed algorithm is capable of handling complex objects with good memory efficiency and accuracy.